Introduction:
Open Science plays a key role in transforming education into a collaborative, inclusive, and globally connected endeavor. The International Astronomy League (LIASTRA Ltda) exemplifies how astronomy-based citizen science can democratize access to knowledge and foster scientific literacy. This study investigates how LIASTRA’s international programs, combining open data, digital tools, and community participation, contribute to educational innovation and engagement in STEM learning.

Methods:
A mixed-methods design was applied, including (1) a bibliometric review of 150 Scopus-indexed publications (2015–2025) on open and citizen science in education, and (2) qualitative case studies of LIASTRA’s initiatives: the Asteroid Search Campaigns, Astronomy Olympiads, Scientific Workshops, Teacher Training Programs, and the Open Sky Virtual Observatory. Data from interviews with 34 educators, students, and institutional partners were analyzed using content analysis and descriptive statistics.

Results:
The findings reveal that open science practices, when integrated into astronomy education, significantly increase STEM engagement and public participation. Over 65% of participants reported higher motivation to pursue science-related careers. Educators identified improvements in inquiry-based teaching, data analysis skills, and collaborative learning.

Conclusion:
LIASTRA’s educational model demonstrates that open data and citizen participation can effectively bridge research and education, promoting inclusivity, digital literacy, and global collaboration. Astronomy thus becomes not only a science of exploration but a pathway for democratizing knowledge, empowering teachers, and inspiring the next generation of global citizens.

Introduction. Educators report that the hardest part of integrating new technology into STEM instruction is knowing how to control it and understand its outputs. This finding recurs across a decade of the authors’ research, from agent-based modeling with NetLogo, through micro:bit programming, through NSA-funded GenCyber camps where most participants embedded cybersecurity in their classrooms, to statewide CS integration (WySLICE) and an NSF-funded RET site (WySTACK). Each intervention introduced a different technology; each exposed the same bottleneck. Generative AI amplifies this: LLMs produce fluent, plausible content requiring verification skills that most STEM educators have never been taught.

Methods. This study synthesizes data across multiple STEM integration PD cohorts. Pre/post self-efficacy measures from earlier WRNN sessions and GenCyber follow-ups provide baseline trajectories. An NSF-funded Noyce workshop series scaffolds a three-stage competency progression, comprising AI-assisted curriculum design, verification of AI-generated STEM content, and applied integration, all using AI tools within authentic disciplinary practice. Instruments capture self-efficacy, verification confidence, and implementation intent. University data (128 course experiences, nine semesters) contextualize the policy environments that educators are preparing students to enter.

Results. Across cohorts and technologies, verification-focused sessions produce the largest self-efficacy gains. Educators completing full progressions describe goals differently than single-session participants, framing AI as something students need to evaluate rather than a tool that saves planning time. University data reinforce this: engineering students in courses with no explicit AI policy report the lowest clarity and highest uncertainty about academic integrity.

Conclusions. Each new technology introduced to educators, NetLogo, micro:bit, GenCyber platforms, and LLMs, required the same scaffolding: guided adoption, structured verification, and disciplinary integration. Self-efficacy gains become most apparent at the verification stage, with novices most challenged by output evaluation. This technology-agnostic competency model, grounded in longitudinal evidence, offers PD designers a replicable framework for whatever classroom technology comes next.

As the demand for publishing in English grows across STEM fields, researchers who use English as a second language face persistent writing challenges, including limited mastery of domain-specific terminology, a tendency toward literal translation, and sentence-length and error issues originating from compact rhetorical habits in their native language. Recent advances in artificial intelligence and data-driven methods offer new opportunities for language instruction and writing support. This study presents a comprehensive review of traditional corpus-building approaches and recent data-driven AI techniques, and then provides a focused case discussion in marine engineering conducted in collaboration with domain researchers. By comparing corpus resources, feature engineering strategies, and modeling approaches, we evaluate the transferability of data-driven instructional models for improving publication readiness among non-native engineering researchers. The discussion highlights evidence-based revision strategies, targeted pathways for disciplinary vocabulary acquisition, and practical ways to integrate automated feedback with expert human guidance to improve manuscript quality. Our review indicates that data-driven AI tools hold promise for supporting terminology acquisition, identifying common error patterns, and generating actionable revision suggestions, but their effectiveness depends on high-quality, domain-specific corpora and close collaboration with subject-matter experts. We conclude with recommendations for future research and pedagogical practice to scale evidence-based ERPP (English for Research Publication Purposes) support across STEM disciplines.

In today’s datafied world, the ability to critically interpret, analyze, and act on data is essential for civic participation and informed decision-making. However, data science literacy—recognized as both a scientific skill and a civic competency—remains underdeveloped in many school systems. The Erasmus+ project Data Science Education in STEAM for Civic Engagement and Social Justice from the Early Years (DataScEd4CiEn) (2023–2026) addresses this challenge by embedding data science into STEAM education for students aged 9–15. Coordinated by the European University Cyprus and funded by the Cyprus Foundation for the Management of European Lifelong Learning Programmes (IDEP), the project brings together partners from Cyprus, Greece, Ireland, Germany, and Spain to develop an innovative framework for integrating data science into STEAM curricula.

A central component of the project is a modular, blended professional development (PD) program designed to empower primary and secondary teachers to collaboratively design and implement data-rich STEAM learning scenarios addressing civic issues such as environmental justice, equity, and socio-economic challenges. Grounded in situated learning and a Communities of Practice approach, the PD program combines interactive workshops, case-based discussions, collaborative design activities, and hands-on exploration of digital tools such as CODAP, Gapminder, and Teachable Machine.

Using a design-based research (DBR) methodology, the study followed an iterative process involving two rounds of implementation and refinement of the PD program. Data from the first pilot implementation in Cyprus were collected through surveys, focus groups, teacher reflections, and classroom observations and analyzed to inform revisions to the course structure, content, and pedagogical strategies before the second implementation. This iterative process allowed the research team to refine the program based on classroom experiences and teacher feedback. Early findings indicate improved teacher confidence and capacity to foster students’ data science literacy, ethical reasoning, and civic awareness through interdisciplinary STEAM learning.

Introduction:
This paper examines the perceptions of early childhood, primary, and secondary school teachers in Spain regarding the existence of a gender gap in subjects related to STEAM (Science, Technology, Engineering, Arts, and Mathematics) disciplines. The main objective is to identify how teachers interpret these inequalities and to what extent they consider them to influence students' academic performance.
Method:
A descriptive study was conducted based on the collection and analysis of responses from teachers at different educational levels in Spain. The questionnaire addressed aspects related to the perception of gender stereotypes, student confidence and motivation in STEAM subjects, and the differential expectations and attitudes between girls and boys.
Results:
The results show that most teachers recognize the existence of perceptible gender differences from the early stages of education. These perceptions suggest that girls tend to show less confidence and motivation in subjects related to technology and engineering, while boys are perceived as more inclined to participate in them. This situation could be influencing future university and professional studies, contributing to the persistence of inequality in these traditionally masculinized STEAM fields.
Conclusions:
The study concludes by highlighting the need to delve deeper into the factors that create and sustain this gap, such as role models, implicit messages in the classroom, and the influence of the social and family environment. It also underscores the importance of designing and implementing pedagogical strategies that promote equal opportunities and spark interest in STEAM disciplines among all students from an early age.

Introduction:
In recent years, particularly after the COVID-19 pandemic, many students have become increasingly disengaged from conventional classroom learning while spending substantial time using digital technologies, especially smartphones. Although smartphones are often considered a source of distraction, their widespread presence also provides an opportunity to integrate technology productively into the learning process. Teacher education programs, therefore, need to equip future educators with strategies to transform commonly used digital devices into effective educational tools. This study proposes a classroom-oriented approach in which smartphone-assisted, low-cost “pocket” experimental setups are used to demonstrate scientific concepts and enhance students’ involvement in science learning.

Methods:
The proposed activity utilizes a compact experimental setup suitable for classroom demonstration and student participation. A simple droplet-based apparatus is used to measure the surface tension of liquids, which, in turn, helps estimate the concentration of dissolved impurities in water. The method can serve as an alternative or complementary approach to conventional polarimetry, which is mainly limited to optically active solutions. In this experiment, droplets produced from a burette or syringe are recorded using a smartphone camera, and droplet formation and detachment are analyzed through video-based visualization techniques. The recorded data enable estimation of droplet parameters and facilitate discussion of key theoretical concepts such as surface tension, density, viscosity, gravitational acceleration, and Reynolds number. Visual analysis tools assist students in observing and interpreting experimental phenomena more effectively.

Results:
Classroom implementation indicates that smartphone-assisted pocket experiments successfully combine theoretical explanations with real-time measurement and visualization. Students demonstrate increased interest and engagement when familiar technology is incorporated into experimental activities. The approach improves conceptual understanding and encourages curiosity, critical thinking, and creativity. For teacher trainees, the activity highlights how low-cost experimental strategies can be used to teach complex scientific concepts effectively, particularly in educational settings with limited laboratory infrastructure.

Traditional STEM instruction often begins with theory, followed by practice. While effective in some contexts, this sequence can feel abstract and disengaging for many learners—especially in Indigenous settings where knowledge is rooted in experience, reflection, and community.

This session introduces a new four-step framework for STEM teaching: Do → Reflect → Explain → Connect. Beginning with hands-on exploration (“Do”), students experience phenomena directly, building curiosity and confidence. Through guided discussion, questioning, and storytelling (“Reflect”), they make sense of patterns in their own words. Educators then layer in scientific principles and vocabulary (“Explain”), grounding learning in disciplinary knowledge. Finally, students extend their understanding to real-world applications and cultural knowledge (“Connect”), seeing STEM as relevant to both their lives and communities.

Participants will explore this model through practical classroom examples drawn from a First Nations school context, such as engineering shelters inspired by traditional designs.

By the end of the session, participants will:

• Understand how “Do → Reflect → Explain → Connect” balances experiential learning, scientific rigor, and cultural relevance.
• Recognize the alignment between this framework and Indigenous pedagogies.
• Take away practical strategies for adapting the model to their own STEM classrooms.

This session offers a fresh, culturally responsive approach to STEM education that honors Indigenous ways of knowing while strengthening student engagement and achievement.

The persistent challenge of demotivation within the realm of English as a Foreign Language (EFL) education has become a focal point for researchers, given its detrimental impact on student engagement and long-term academic achievement. Understanding the psychological antecedents of this phenomenon is critical for developing effective intervention strategies. This research specifically delves into the intricate interplay between demotivation, self-esteem, and students’ perceived English teacher support, constructing a conceptual framework to examine how various dimensions of teacher support influence demotivation through the mediating mechanism of self-esteem.

This quantitative study involved a sample of 628 senior high school students from northern China. Data were meticulously collected using validated Likert-scale questionnaires assessing three core dimensions: perceived English teacher support (emotional, instrumental, and academic), self-esteem, and demotivation. The complex structural relationships among these latent variables were examined using Structural Equation Modeling (SEM).

The findings revealed a nuanced picture of teacher influence: emotional support and instrumental support were found to significantly and indirectly reduce demotivation by bolstering students’ self-esteem. This suggests that when students feel psychologically secure and practically assisted, their self-worth increases, creating a buffer against demotivating factors. In contrast, academic support did not yield a significant effect on either self-esteem or demotivation in this specific context. These results underscore the vital importance of the affective and practical dimensions of teaching. The study concludes that fostering a supportive emotional climate and providing tangible learning tools are essential for enhancing learners’ self-esteem and, consequently, mitigating the erosion of motivation in the English language classroom.

Early childhood teacher training in Spain prepares educational professionals to work with children from birth to six years old. This stage is divided into two cycles: the first (from 0-2 years) and the second (from 3 to 6 years). Therefore, anyone trained in this field should possess professional skills across this entire age range. However, reality shows a bias towards training geared towards the older children (4-5 years). This is due to multiple factors, but primarily stems from the specific characteristics of children at these ages and the limited research on this period of human development. In this paper, we aim to present some science-based learning activities implemented with 1- and 2-year-olds. These activities were developed within a broader research project on science education in early childhood, involving university researchers, teachers, and educators working with this age group. The results show that it is possible to begin working on scientific processes at these ages by taking into account their individual characteristics. To illustrate this, we present two specific experiments: scientific diapers and the cardboard city. In the first, diapers were used as a familiar object to explore scientific concepts with 2-year-olds, and in the second, we were able to observe the differences between 1- and 2-year-olds. These results are fundamental for initial teacher training, since future teachers must also possess scientific competencies relevant to these age groups.

The characteristics and supportive behaviour of senior human resources in higher education are a relevant topic in society, with a growing number of aging people. The role and the number of university teachers increase with the expansion, massification, and internationalization of higher education. A scoping systematic review of teacher research, covering the period 2010-2019, highlighted a gap in empirical, evidence-based research in this field. In our previous research, we analysed academics' work–life conflict and found a negative correlation with the institutional social support factor. I explore here two research questions: What are the characteristics of senior academic workers? How do they support fellow teachers in their career development? We consider “seniors,” teachers, and researchers working in different higher education fields aged 45 or above. The Centre for Higher Education Research and Development – Hungary collected data from universities in Romania, Hungary, Slovakia, Serbia, and Ukraine using a questionnaire titled Central and Eastern European Teachers in Higher Education (CEETHE 2023). We analysed the data using SPSS (principal factor analyses and correlations). A total of 432 respondents were seniors aged 45 and older. More than half of the teachers discussed educational, scientific, or other topics with a fellow teacher or worked as a co-author in a research group, not characteristic discussions about future plans; to arrange a meeting outside of work; or support in preparing for a doctorate, habilitation, or tender. The majority of seniors (56%) did not work with a fellow teacher on an online workspace. Principal component analyses yielded two factors: 1) informal conversations and 2) working partners' behaviour. There is a significant and positive correlation between informal conversations and work satisfaction; working partnership relations with working hours; and multiple roles of teachers. Working partnership behaviour and mentor relations with a fellow are additional roles that require additional work and time.